基于Q学习的星地融合协作传输中继选择策略

Q-learning Based Relay Selection Strategy for Hybrid Satellite-Terrestrial Cooperative Transmission

协作网络中的中继技术能够实现空间分集,但中继选择会对系统性能产生较大影响。针对这一问题,本文提出了一种基于Q学习的星地融合协作传输中继选择策略。首先,所有中继节点在经过放大转发协议的情况下,在接收端得到最大比合并后的输出信噪比表达式。然后,设定Q学习的状态、动作和奖励函数,选择累积回报最大的中继节点。接着,为了遍历所有状态,引入了Boltzmann选择策略,用概率的途径来选择动作,使源节点探索所有状态并利用最优状态。最后,在所选中继节点与源节点之间进行功率分配得到最优传输功率。仿真结果表明:与随机中继选择算法相比,所提出的Q学习中继选择策略对系统性能有较大地提升。

Cooperative relay networks can achieve spatial diversity,but their system per-formances heavily depends on relay selection schemes.To solve this problem,a hybrid satellite-terrestrial cooperative network relay selection strategy based on Q-learning is pro-posed.First,under the consideration that all the relay nodes employ amplify-and-forward protocol,the end-to-end output signal-to-noise ratio after combining the maximal ratio is derived.Next,the state,action and reward function of Q-learning are set to select the relay node with the greatest cumulative return.Then,in order to traverse all states,Boltzmann selection policy is induced to select action by probability approach,so that the source node can explore all states and find the optimal one.Finally,the optimal transmission power is obtained by using power allocation scheme between the selected relay node and the source node.Simulation results show that,compared with the random relay selection algorithm,the proposed strategy greatly improves the system performance.